Selecting rows (loc, iloc) in Data Analysis Python - Step-by-Step Execution

Concept Flow - Selecting rows (loc, iloc)

Start with DataFrame

↓

Choose selection method

↓

loc

↓

Select rows by

↓

label/index

↓

Return selected rows

↓

End

You start with a DataFrame, choose either loc or iloc to select rows by label or position, then get the selected rows.

Execution Sample

Data Analysis Python

import pandas as pd

df = pd.DataFrame({
    'Name': ['Anna', 'Bob', 'Cara', 'Dan'],
    'Age': [23, 35, 45, 28]
})

row_loc = df.loc[1]
row_iloc = df.iloc[1]

This code creates a DataFrame and selects the second row using loc and iloc.

Execution Table

Step	Action	Selection Method	Index Used	Resulting Row
1	Create DataFrame	-	-	{0: Name=Anna, Age=23; 1: Name=Bob, Age=35; 2: Name=Cara, Age=45; 3: Name=Dan, Age=28}
2	Select row with loc	loc	1 (label)	{Name: Bob, Age: 35}
3	Select row with iloc	iloc	1 (position)	{Name: Bob, Age: 35}
4	Select row with loc	loc	3 (label)	{Name: Dan, Age: 28}
5	Select row with iloc	iloc	3 (position)	{Name: Dan, Age: 28}
6	Try loc with label not in index	loc	5 (label)	KeyError raised
7	Try iloc with position out of range	iloc	5 (position)	IndexError raised

💡 Execution stops after errors or when all selections are done.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	After Step 5
df	Empty	{0: Anna, 23; 1: Bob, 35; 2: Cara, 45; 3: Dan, 28}	{0: Anna, 23; 1: Bob, 35; 2: Cara, 45; 3: Dan, 28}	{0: Anna, 23; 1: Bob, 35; 2: Cara, 45; 3: Dan, 28}	{0: Anna, 23; 1: Bob, 35; 2: Cara, 45; 3: Dan, 28}
row_loc	Undefined	{Name: Bob, Age: 35}	{Name: Bob, Age: 35}	{Name: Dan, Age: 28}	{Name: Dan, Age: 28}
row_iloc	Undefined	Undefined	{Name: Bob, Age: 35}	{Name: Bob, Age: 35}	{Name: Dan, Age: 28}

Key Moments - 3 Insights

Why does df.loc[5] cause an error but df.iloc[5] also causes an error?

Both cause errors because loc looks for a label '5' which does not exist in the index, and iloc looks for position 5 which is outside the DataFrame range. See execution_table rows 6 and 7.

Does df.loc[1] select the same row as df.iloc[1]?

Yes, in this example both select the second row because the index labels are default integers matching positions. See execution_table rows 2 and 3.

What is the difference between loc and iloc in selecting rows?

loc selects rows by index label, iloc selects rows by integer position. This means loc depends on the DataFrame's index labels, iloc always uses zero-based positions. See concept_flow and execution_table.

Visual Quiz - 3 Questions

Look at the execution_table at step 3. What row does df.iloc[1] select?

A Row with Name 'Cara' and Age 45

B Row with Name 'Bob' and Age 35

C Row with Name 'Anna' and Age 23

D Row with Name 'Dan' and Age 28

💡 Hint

Check the 'Resulting Row' column at step 3 in execution_table.

At which step does df.loc[5] cause an error?

A Step 4

B Step 5

C Step 6

D Step 7

💡 Hint

Look for 'KeyError raised' in the execution_table.

If the DataFrame index was ['a', 'b', 'c', 'd'], what would df.loc[1] do?

A Raise a KeyError

B Select the second row

C Select the first row

D Select the row with label '1'

💡 Hint

Recall loc selects by label, so if '1' is not an index label, it raises an error.

Concept Snapshot

Selecting rows in pandas:
- Use df.loc[label] to select by index label.
- Use df.iloc[position] to select by integer position.
- loc includes the label; iloc uses zero-based position.
- loc raises KeyError if label not found.
- iloc raises IndexError if position out of range.

Full Transcript

This lesson shows how to select rows from a pandas DataFrame using loc and iloc. The DataFrame has four rows with default integer index labels 0 to 3. Using df.loc[1] selects the row with label 1, which is the second row. Using df.iloc[1] selects the row at position 1, also the second row. Both return the row with Name 'Bob' and Age 35. Trying to select df.loc[5] causes a KeyError because label 5 does not exist. Trying df.iloc[5] causes an IndexError because position 5 is outside the DataFrame. The key difference is loc uses labels, iloc uses positions. This is important when the index is not default integers. Understanding these helps avoid errors and select data correctly.